PYTHON PROGRAMMNG

Consider a single dimension. Obtain N = 100iidsamples of x uniformly randomly between1 and 10. The corresponding y values are obtained as the logarithm of x plus a Gaussian noise (mean 0, standard deviation 0.1). Now use K-NN regression (for each of the following three schemes, and with K = 1, 3, 50 for each scheme) to obtain estimates of y at x-values of1, 3, 5, 7 and 9:the K neighbors contribute equallyeach of the K neighbors has an influence that is inversely proportional to the distance from the pointall the N points contribute, with each contribution proportional toe12d2, where d represents distance.